{"id":15482789,"url":"https://github.com/ap6yc/mri-tl-ml","last_synced_at":"2025-03-28T15:20:51.670Z","repository":{"id":128626823,"uuid":"319402198","full_name":"AP6YC/MRI-TL-ML","owner":"AP6YC","description":"A study of the efficacy of transfer learning methods versus \"traditional\" machine learning methods (i.e., separate feature extraction and learner architectures).","archived":false,"fork":false,"pushed_at":"2020-12-11T22:32:00.000Z","size":6509,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-02-02T15:31:27.828Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AP6YC.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-12-07T18:00:46.000Z","updated_at":"2020-12-11T22:32:03.000Z","dependencies_parsed_at":"2023-04-29T00:57:17.366Z","dependency_job_id":null,"html_url":"https://github.com/AP6YC/MRI-TL-ML","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AP6YC%2FMRI-TL-ML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AP6YC%2FMRI-TL-ML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AP6YC%2FMRI-TL-ML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AP6YC%2FMRI-TL-ML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AP6YC","download_url":"https://codeload.github.com/AP6YC/MRI-TL-ML/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246049641,"owners_count":20715512,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-10-02T05:10:00.461Z","updated_at":"2025-03-28T15:20:51.665Z","avatar_url":"https://github.com/AP6YC.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MRI-TL-ML\nA study of the efficacy of transfer learning methods versus \"traditional\" machine learning methods (i.e., separate feature extraction and learner architectures).\n\nThis project is a submission of Sasha Petrenko for his MATH5001: Mathematics of Medical Imaging course and the Missouri University of Science and Technology.\n\n## Usage\n\n1. Download the dataset at https://figshare.com/articles/dataset/brain_tumor_dataset/1512427\n2. Extract all of the data to a single folder.\n3. Preprocess the images with the script `matlab/preprocessing.m`, pointing to the correct directory.\n4. Create a python environment (e.g., `conda`) and install the requirements under `requirements.txt`.\n5. Run the notebook `notebooks/tl-mri.ipyng`.\n6. View the figures and results in `results/`.\n\n## Author\n\n* Sasha Petrenko \u003csap625@mst.edu\u003e","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fap6yc%2Fmri-tl-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fap6yc%2Fmri-tl-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fap6yc%2Fmri-tl-ml/lists"}